Hidden markov model matlab code for biome data trial#
A phase 2 trial of the somatostatin analog pasireotide to prevent GI toxicity and acute GVHD in allogeneic hematopoietic stem cell transplant. Ramalingam S, Siamakpour-Reihani S, Bohannan L, Ren Y, Sibley, Sheng J, Ma L, Nixon AB, Lyu J, Parker DC, Bain B, Muehlbauer M, Ilkayeva O, Kraus VB, Huebner J, Spitzer T, Brown J, Peled J, van den Brink M, Gomes A, Choi T, Gasparetto C, Horwitz M, Long G, Lopez R, Rizzieri D, Sarantopoulos S, Chao N, and Sung AD. The urinary microbiome in postmenopausal women with recurrent urinary tract infections. Vaughan M, Mao J, Karstens L, Ma L, Amundsen C, Schmader K, Siddiqui N. IEEE Transactions on Pattern Analysis and Machine Intelligence. (2021) Learning asymmetric and local features in multi-dimensional data through wavelets with recursive partitioning. (2021) Dirichlet-tree multinomial mixtures for clustering microbiome compositions. Quantitative occupancy of myriad transcription factors from one DNase experiment enables efficient comparisons across conditions Publications Luo K, Zhong J, Safi A, Hong L.K., Tewari A.K., Song L, Reddy T.E., Ma L, Crawford G.E., and Hartemink A.J. (2021) Updating urinary microbiome analyses to enhance biologic interpretation. Siddiqui N, Ma L, Brubaker L, Mao J, Hoffman C, Karstens L. ( This is the journal version of our previous CVPR paper.) (2021) Efficient in-situ image and video compression through probabilistic image representation. (2021) Multiscale Fisher's independence test for multivariate dependence. (2021) Coarsened mixtures of hierarchical skew normal kernels for flow cytometry analyses. (2021) Hidden Markov Pólya trees for high-dimensional distributions. Winner of a student/postdoc best paper award at 2021 ISBA World Meeting.Īwaya N and Ma L.
(2021) Tree boosting for learning probability measures. (2021) Microbiome compositional analysis with logistic-tree normal models. (2021) Microbiome subcommunity learning with logistic-tree normal latent Dirichlet allocation.
Prior support: NSF grants DMS-1309057, DMS-1612889, and a Google Faculty Research Award. My research group is currently supported by both the NSF (Statistics Program grant DMS-2013930 and CAREER Award grant DMS-1749789) and the NIH (NIGMS grant R01-GM135440).
In particular, current efforts have been devoted to modeling and analyzing data from microbiome sequencing experiments and flow cytometry. My applied interest focuses on modeling complex data sets from biomedical experiments. Multi-scale inference provides a general framework for tackling the computational bottleneck, while preserving the theoretical guarantees enjoyed by classical methods. Traditional nonparametric approaches, while enjoying many established theoretical properties, are often computationally intractable for big data. Statistical modeling of biomedical data sets, especially microbiome sequencing data and flow cytometryĪ recent methodological focus of my research is on using multi-scale techniques to construct flexible probability models that can be applied to massive data sets. Recursive partitioning and tree-related methods